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<main>
<article id="content">
<header>
<h1 class="title">Module <code>pandas_profiling.model.messages</code></h1>
</header>
<section id="section-intro">
<p>Logic for alerting the user on possibly problematic patterns in the data (e.g. high number of zeros , constant
values, high correlations).</p>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">&#34;&#34;&#34;Logic for alerting the user on possibly problematic patterns in the data (e.g. high number of zeros , constant
values, high correlations).&#34;&#34;&#34;
from enum import Enum, unique
from typing import List
import warnings
from contextlib import suppress
import numpy as np
import re
from dateutil.parser import parse

from pandas_profiling.config import config
from pandas_profiling.model.base import Variable


@unique
class MessageType(Enum):
    &#34;&#34;&#34;Message Types&#34;&#34;&#34;

    CONST = 1
    &#34;&#34;&#34;This variable has a constant value.&#34;&#34;&#34;

    ZEROS = 2
    &#34;&#34;&#34;This variable contains zeros.&#34;&#34;&#34;

    CORR = 3
    &#34;&#34;&#34;This variable is highly correlated.&#34;&#34;&#34;

    RECODED = 4
    &#34;&#34;&#34;This variable is correlated (categorical).&#34;&#34;&#34;

    HIGH_CARDINALITY = 5
    &#34;&#34;&#34;This variable has a high cardinality.&#34;&#34;&#34;

    UNSUPPORTED = 6
    &#34;&#34;&#34;This variable is unsupported.&#34;&#34;&#34;

    DUPLICATES = 7
    &#34;&#34;&#34;This variable contains duplicates.&#34;&#34;&#34;

    SKEWED = 8
    &#34;&#34;&#34;This variable is highly skewed.&#34;&#34;&#34;

    MISSING = 9
    &#34;&#34;&#34;THis variable contains missing values.&#34;&#34;&#34;

    INFINITE = 10
    &#34;&#34;&#34;This variable contains infinite values.&#34;&#34;&#34;

    TYPE_DATE = 11
    &#34;&#34;&#34;This variable is likely a datetime, but treated as categorical.&#34;&#34;&#34;


class Message(object):
    &#34;&#34;&#34;A message object (type, values, column).&#34;&#34;&#34;

    def __init__(
        self, message_type: MessageType, values: dict, column_name: str or None = None
    ):
        self.message_type = message_type
        self.values = values
        self.column_name = column_name


def check_table_messages(table: dict) -&gt; List[Message]:
    &#34;&#34;&#34;Checks the overall dataset for warnings.

    Args:
        table: Overall dataset statistics.

    Returns:
        A list of messages.
    &#34;&#34;&#34;
    messages = []
    if warning_value(table[&#34;n_duplicates&#34;]):
        messages.append(Message(message_type=MessageType.DUPLICATES, values=table))
    return messages


def check_variable_messages(col: str, description: dict) -&gt; List[Message]:
    &#34;&#34;&#34;Checks individual variables for warnings.

    Args:
        col: The column name that is checked.
        description: The series description.

    Returns:
        A list of messages.
    &#34;&#34;&#34;
    messages = []
    # Special types
    if description[&#34;type&#34;] in {
        Variable.S_TYPE_UNSUPPORTED,
        Variable.S_TYPE_CORR,
        Variable.S_TYPE_CONST,
        Variable.S_TYPE_RECODED,
    }:
        messages.append(
            Message(
                column_name=col,
                message_type=MessageType[description[&#34;type&#34;].value],
                values=description,
            )
        )

    if description[&#34;type&#34;] in {Variable.TYPE_CAT, Variable.S_TYPE_UNIQUE}:
        if description[&#34;date_warning&#34;]:
            messages.append(
                Message(column_name=col, message_type=MessageType.TYPE_DATE, values={})
            )

    if description[&#34;type&#34;] in {Variable.TYPE_CAT, Variable.TYPE_BOOL}:
        # High cardinality
        if description[&#34;distinct_count&#34;] &gt; config[&#34;cardinality_threshold&#34;].get(int):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.HIGH_CARDINALITY,
                    values=description,
                )
            )

    if description[&#34;type&#34;] in {Variable.TYPE_NUM}:
        # Skewness
        if warning_skewness(description[&#34;skewness&#34;]):
            messages.append(
                Message(
                    column_name=col, message_type=MessageType.SKEWED, values=description
                )
            )
        # Zeros
        if warning_value(description[&#34;p_zeros&#34;]):
            messages.append(
                Message(
                    column_name=col, message_type=MessageType.ZEROS, values=description
                )
            )

    if description[&#34;type&#34;] not in {
        Variable.S_TYPE_UNSUPPORTED,
        Variable.S_TYPE_CORR,
        Variable.S_TYPE_CONST,
        Variable.S_TYPE_RECODED,
    }:
        # Missing
        if warning_value(description[&#34;p_missing&#34;]):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.MISSING,
                    values=description,
                )
            )
        # Infinite values
        if warning_value(description[&#34;p_infinite&#34;]):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.INFINITE,
                    values=description,
                )
            )

    return messages


def warning_value(value: np.nan or float) -&gt; bool:
    return not np.isnan(value) and value &gt; 0.01


def warning_skewness(v: np.nan or float) -&gt; bool:
    return not np.isnan(v) and (
        v &lt; -config[&#34;vars&#34;][&#34;num&#34;][&#34;skewness_threshold&#34;].get(int)
        or v &gt; config[&#34;vars&#34;][&#34;num&#34;][&#34;skewness_threshold&#34;].get(int)
    )


def _date_parser(date_string):
    pattern = re.compile(r&#34;[.\-:]&#34;)
    pieces = re.split(pattern, date_string)

    if len(pieces) &lt; 3:
        raise ValueError(&#34;Must have at least year, month and date passed&#34;)

    return parse(date_string)


def warning_type_date(series):
    with suppress(ValueError):
        with suppress(TypeError):
            series.apply(_date_parser)
            return True

    return False</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="pandas_profiling.model.messages.check_table_messages"><code class="name flex">
<span>def <span class="ident">check_table_messages</span></span>(<span>table)</span>
</code></dt>
<dd>
<section class="desc"><p>Checks the overall dataset for warnings.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>table</code></strong></dt>
<dd>Overall dataset statistics.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>A list of messages.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def check_table_messages(table: dict) -&gt; List[Message]:
    &#34;&#34;&#34;Checks the overall dataset for warnings.

    Args:
        table: Overall dataset statistics.

    Returns:
        A list of messages.
    &#34;&#34;&#34;
    messages = []
    if warning_value(table[&#34;n_duplicates&#34;]):
        messages.append(Message(message_type=MessageType.DUPLICATES, values=table))
    return messages</code></pre>
</details>
</dd>
<dt id="pandas_profiling.model.messages.check_variable_messages"><code class="name flex">
<span>def <span class="ident">check_variable_messages</span></span>(<span>col, description)</span>
</code></dt>
<dd>
<section class="desc"><p>Checks individual variables for warnings.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>col</code></strong></dt>
<dd>The column name that is checked.</dd>
<dt><strong><code>description</code></strong></dt>
<dd>The series description.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>A list of messages.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def check_variable_messages(col: str, description: dict) -&gt; List[Message]:
    &#34;&#34;&#34;Checks individual variables for warnings.

    Args:
        col: The column name that is checked.
        description: The series description.

    Returns:
        A list of messages.
    &#34;&#34;&#34;
    messages = []
    # Special types
    if description[&#34;type&#34;] in {
        Variable.S_TYPE_UNSUPPORTED,
        Variable.S_TYPE_CORR,
        Variable.S_TYPE_CONST,
        Variable.S_TYPE_RECODED,
    }:
        messages.append(
            Message(
                column_name=col,
                message_type=MessageType[description[&#34;type&#34;].value],
                values=description,
            )
        )

    if description[&#34;type&#34;] in {Variable.TYPE_CAT, Variable.S_TYPE_UNIQUE}:
        if description[&#34;date_warning&#34;]:
            messages.append(
                Message(column_name=col, message_type=MessageType.TYPE_DATE, values={})
            )

    if description[&#34;type&#34;] in {Variable.TYPE_CAT, Variable.TYPE_BOOL}:
        # High cardinality
        if description[&#34;distinct_count&#34;] &gt; config[&#34;cardinality_threshold&#34;].get(int):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.HIGH_CARDINALITY,
                    values=description,
                )
            )

    if description[&#34;type&#34;] in {Variable.TYPE_NUM}:
        # Skewness
        if warning_skewness(description[&#34;skewness&#34;]):
            messages.append(
                Message(
                    column_name=col, message_type=MessageType.SKEWED, values=description
                )
            )
        # Zeros
        if warning_value(description[&#34;p_zeros&#34;]):
            messages.append(
                Message(
                    column_name=col, message_type=MessageType.ZEROS, values=description
                )
            )

    if description[&#34;type&#34;] not in {
        Variable.S_TYPE_UNSUPPORTED,
        Variable.S_TYPE_CORR,
        Variable.S_TYPE_CONST,
        Variable.S_TYPE_RECODED,
    }:
        # Missing
        if warning_value(description[&#34;p_missing&#34;]):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.MISSING,
                    values=description,
                )
            )
        # Infinite values
        if warning_value(description[&#34;p_infinite&#34;]):
            messages.append(
                Message(
                    column_name=col,
                    message_type=MessageType.INFINITE,
                    values=description,
                )
            )

    return messages</code></pre>
</details>
</dd>
<dt id="pandas_profiling.model.messages.warning_skewness"><code class="name flex">
<span>def <span class="ident">warning_skewness</span></span>(<span>v)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def warning_skewness(v: np.nan or float) -&gt; bool:
    return not np.isnan(v) and (
        v &lt; -config[&#34;vars&#34;][&#34;num&#34;][&#34;skewness_threshold&#34;].get(int)
        or v &gt; config[&#34;vars&#34;][&#34;num&#34;][&#34;skewness_threshold&#34;].get(int)
    )</code></pre>
</details>
</dd>
<dt id="pandas_profiling.model.messages.warning_type_date"><code class="name flex">
<span>def <span class="ident">warning_type_date</span></span>(<span>series)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def warning_type_date(series):
    with suppress(ValueError):
        with suppress(TypeError):
            series.apply(_date_parser)
            return True

    return False</code></pre>
</details>
</dd>
<dt id="pandas_profiling.model.messages.warning_value"><code class="name flex">
<span>def <span class="ident">warning_value</span></span>(<span>value)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def warning_value(value: np.nan or float) -&gt; bool:
    return not np.isnan(value) and value &gt; 0.01</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="pandas_profiling.model.messages.Message"><code class="flex name class">
<span>class <span class="ident">Message</span></span>
<span>(</span><span>message_type, values, column_name=None)</span>
</code></dt>
<dd>
<section class="desc"><p>A message object (type, values, column).</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class Message(object):
    &#34;&#34;&#34;A message object (type, values, column).&#34;&#34;&#34;

    def __init__(
        self, message_type: MessageType, values: dict, column_name: str or None = None
    ):
        self.message_type = message_type
        self.values = values
        self.column_name = column_name</code></pre>
</details>
</dd>
<dt id="pandas_profiling.model.messages.MessageType"><code class="flex name class">
<span>class <span class="ident">MessageType</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Message Types</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class MessageType(Enum):
    &#34;&#34;&#34;Message Types&#34;&#34;&#34;

    CONST = 1
    &#34;&#34;&#34;This variable has a constant value.&#34;&#34;&#34;

    ZEROS = 2
    &#34;&#34;&#34;This variable contains zeros.&#34;&#34;&#34;

    CORR = 3
    &#34;&#34;&#34;This variable is highly correlated.&#34;&#34;&#34;

    RECODED = 4
    &#34;&#34;&#34;This variable is correlated (categorical).&#34;&#34;&#34;

    HIGH_CARDINALITY = 5
    &#34;&#34;&#34;This variable has a high cardinality.&#34;&#34;&#34;

    UNSUPPORTED = 6
    &#34;&#34;&#34;This variable is unsupported.&#34;&#34;&#34;

    DUPLICATES = 7
    &#34;&#34;&#34;This variable contains duplicates.&#34;&#34;&#34;

    SKEWED = 8
    &#34;&#34;&#34;This variable is highly skewed.&#34;&#34;&#34;

    MISSING = 9
    &#34;&#34;&#34;THis variable contains missing values.&#34;&#34;&#34;

    INFINITE = 10
    &#34;&#34;&#34;This variable contains infinite values.&#34;&#34;&#34;

    TYPE_DATE = 11
    &#34;&#34;&#34;This variable is likely a datetime, but treated as categorical.&#34;&#34;&#34;</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>enum.Enum</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="pandas_profiling.model.messages.MessageType.CONST"><code class="name">var <span class="ident">CONST</span></code></dt>
<dd>
<section class="desc"><p>This variable has a constant value.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.CORR"><code class="name">var <span class="ident">CORR</span></code></dt>
<dd>
<section class="desc"><p>This variable is highly correlated.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.DUPLICATES"><code class="name">var <span class="ident">DUPLICATES</span></code></dt>
<dd>
<section class="desc"><p>This variable contains duplicates.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.HIGH_CARDINALITY"><code class="name">var <span class="ident">HIGH_CARDINALITY</span></code></dt>
<dd>
<section class="desc"><p>This variable has a high cardinality.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.INFINITE"><code class="name">var <span class="ident">INFINITE</span></code></dt>
<dd>
<section class="desc"><p>This variable contains infinite values.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.MISSING"><code class="name">var <span class="ident">MISSING</span></code></dt>
<dd>
<section class="desc"><p>THis variable contains missing values.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.RECODED"><code class="name">var <span class="ident">RECODED</span></code></dt>
<dd>
<section class="desc"><p>This variable is correlated (categorical).</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.SKEWED"><code class="name">var <span class="ident">SKEWED</span></code></dt>
<dd>
<section class="desc"><p>This variable is highly skewed.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.TYPE_DATE"><code class="name">var <span class="ident">TYPE_DATE</span></code></dt>
<dd>
<section class="desc"><p>This variable is likely a datetime, but treated as categorical.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.UNSUPPORTED"><code class="name">var <span class="ident">UNSUPPORTED</span></code></dt>
<dd>
<section class="desc"><p>This variable is unsupported.</p></section>
</dd>
<dt id="pandas_profiling.model.messages.MessageType.ZEROS"><code class="name">var <span class="ident">ZEROS</span></code></dt>
<dd>
<section class="desc"><p>This variable contains zeros.</p></section>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="pandas_profiling.model" href="index.html">pandas_profiling.model</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="pandas_profiling.model.messages.check_table_messages" href="#pandas_profiling.model.messages.check_table_messages">check_table_messages</a></code></li>
<li><code><a title="pandas_profiling.model.messages.check_variable_messages" href="#pandas_profiling.model.messages.check_variable_messages">check_variable_messages</a></code></li>
<li><code><a title="pandas_profiling.model.messages.warning_skewness" href="#pandas_profiling.model.messages.warning_skewness">warning_skewness</a></code></li>
<li><code><a title="pandas_profiling.model.messages.warning_type_date" href="#pandas_profiling.model.messages.warning_type_date">warning_type_date</a></code></li>
<li><code><a title="pandas_profiling.model.messages.warning_value" href="#pandas_profiling.model.messages.warning_value">warning_value</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="pandas_profiling.model.messages.Message" href="#pandas_profiling.model.messages.Message">Message</a></code></h4>
</li>
<li>
<h4><code><a title="pandas_profiling.model.messages.MessageType" href="#pandas_profiling.model.messages.MessageType">MessageType</a></code></h4>
<ul class="two-column">
<li><code><a title="pandas_profiling.model.messages.MessageType.CONST" href="#pandas_profiling.model.messages.MessageType.CONST">CONST</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.CORR" href="#pandas_profiling.model.messages.MessageType.CORR">CORR</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.DUPLICATES" href="#pandas_profiling.model.messages.MessageType.DUPLICATES">DUPLICATES</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.HIGH_CARDINALITY" href="#pandas_profiling.model.messages.MessageType.HIGH_CARDINALITY">HIGH_CARDINALITY</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.INFINITE" href="#pandas_profiling.model.messages.MessageType.INFINITE">INFINITE</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.MISSING" href="#pandas_profiling.model.messages.MessageType.MISSING">MISSING</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.RECODED" href="#pandas_profiling.model.messages.MessageType.RECODED">RECODED</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.SKEWED" href="#pandas_profiling.model.messages.MessageType.SKEWED">SKEWED</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.TYPE_DATE" href="#pandas_profiling.model.messages.MessageType.TYPE_DATE">TYPE_DATE</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.UNSUPPORTED" href="#pandas_profiling.model.messages.MessageType.UNSUPPORTED">UNSUPPORTED</a></code></li>
<li><code><a title="pandas_profiling.model.messages.MessageType.ZEROS" href="#pandas_profiling.model.messages.MessageType.ZEROS">ZEROS</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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